{"id":"W2883393561","doi":"10.1002/smr.1965","title":"Program comprehension through reverse‐engineered sequence diagrams: A systematic review","year":2018,"lang":"en","type":"review","venue":"Journal of Software Evolution and Process","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Sequence diagram; Program comprehension; Documentation; Reverse engineering; Sequence (biology); Process (computing); Set (abstract data type); Context (archaeology); Software engineering; Software; Comprehension; Use Case Diagram; Data science; Information retrieval; Programming language; Data mining; Unified Modeling Language; Software system; Class diagram","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001557086,0.0005427399,0.002763651,0.0003588099,0.0001405484,0.0002282864,0.001681989,0.0003112515,0.000006656124],"category_scores_gemma":[0.005067322,0.0003834264,0.0005153252,0.001600463,0.0001258354,0.001011488,0.0002488144,0.0009015636,0.00003812556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003672456,"about_ca_system_score_gemma":0.0008483218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002819659,"about_ca_topic_score_gemma":3.62511e-7,"domain_scores_codex":[0.9958653,0.0003543904,0.001692373,0.0004961282,0.001124969,0.0004668622],"domain_scores_gemma":[0.9949551,0.001148097,0.001526251,0.0007308508,0.00136716,0.0002725557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000001715645,0.00005908456,0.000005430508,0.955129,0.0001522323,0.00004627307,0.00009034041,0.000003908552,2.894248e-8,0.00006870779,0.002346803,0.04209653],"study_design_scores_gemma":[0.0001743375,0.0003656474,0.000004830191,0.8672214,0.0007881031,0.002333259,0.000007340333,0.000190559,2.420512e-7,0.0002733572,0.1281724,0.0004685366],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001506871,0.8395888,0.1577822,0.00004736218,0.0006126731,0.001679126,0.000007366378,0.000278252,0.000002791216],"genre_scores_gemma":[0.00001600751,0.9652041,0.03419444,0.00006366914,0.0002502702,0.0001680041,0.000007869045,0.0000460027,0.00004962205],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1258256,"threshold_uncertainty_score":0.9998618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06289656528401565,"score_gpt":0.3715168890266922,"score_spread":0.3086203237426766,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}